library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.6 ✓ dplyr 1.0.4
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(palmerpenguins)
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Ade…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgers…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1,…
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1,…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 18…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475,…
## $ sex <fct> male, female, female, NA, female, male, female, mal…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 200…
str(penguins)
## tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
## $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
## $ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
## $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
## $ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
## $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
nrow(penguins)
## [1] 344
ncol(penguins)
## [1] 8
penguins
## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torge… 39.1 18.7 181 3750
## 2 Adelie Torge… 39.5 17.4 186 3800
## 3 Adelie Torge… 40.3 18 195 3250
## 4 Adelie Torge… NA NA NA NA
## 5 Adelie Torge… 36.7 19.3 193 3450
## 6 Adelie Torge… 39.3 20.6 190 3650
## 7 Adelie Torge… 38.9 17.8 181 3625
## 8 Adelie Torge… 39.2 19.6 195 4675
## 9 Adelie Torge… 34.1 18.1 193 3475
## 10 Adelie Torge… 42 20.2 190 4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
ggplot(data = penguins,
mapping = aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species,
shape = species,
size = body_mass_g,
alpha = flipper_length_mm)) +
geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
size = body_mass_g,
alpha = flipper_length_mm)) +
geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm)) +
geom_point(size = 5, alpha = 0.5)
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm)) +
geom_point() +
facet_grid(species ~ island)
## Warning: Removed 2 rows containing missing values (geom_point).
In den nächsten Code-chunks habe ich bereits Code für dich vorbereitet. Beschreibe was in den Plots vorgeht und wie der Code in Beziehung zum Output steht.
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_grid(species ~ sex)
## Warning: Removed 2 rows containing missing values (geom_point).
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_grid(sex ~ species)
## Warning: Removed 2 rows containing missing values (geom_point).
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_wrap(~ species)
## Warning: Removed 2 rows containing missing values (geom_point).
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_grid(. ~ species)
## Warning: Removed 2 rows containing missing values (geom_point).
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_wrap(~ species)
## Warning: Removed 2 rows containing missing values (geom_point).
Lösche diesen Text und beschreibe was in den Plots und im Code vorgeht
ggplot(penguins,
aes(x = bill_depth_mm,
y = bill_length_mm)) +
geom_point() +
facet_wrap(~ species, ncol = 2)
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species)) +
geom_point() +
scale_color_viridis_d()
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species)) +
geom_point() +
scale_color_brewer(type = "qualitative", palette = "Set1")
## Warning: Removed 2 rows containing missing values (geom_point).
# Farben mit Namen
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species)) +
geom_point() +
scale_color_manual(values = c("red", "blue", "green"))
## Warning: Removed 2 rows containing missing values (geom_point).
# Hex colors
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species)) +
geom_point() +
scale_color_manual(values = c('#9EB0FFFF', '#180B09FF', '#FFACACFF'))
## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
colour = species)) +
geom_point() +
scale_color_viridis_d() +
scale_x_continuous(breaks = seq(0, 70, 5), limits = c(0, 70)) +
scale_y_continuous(breaks = seq(0, 30, 2.5), limits = c(0, 30))
## Warning: Removed 2 rows containing missing values (geom_point).
seq(from = 0, to = 70, by = 5)
## [1] 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
c(0, 70)
## [1] 0 70
Fülle die Lücken aus
ggplot(___,
___(x = ___,
___ = species)) +
geom____() +
facet_grid(___ ~ ___) +
scale_fill_brewer(type = "qual", palette = "Set2") +
___(___ = "none")
ggplot(penguins,
aes(x = island,
fill = species)) +
geom_bar() +
facet_grid(sex ~ species) +
scale_fill_brewer(type = "qual", palette = "Set2") +
theme(legend.position = "none")
Du kannst jederzeit einen Git commit durchführen und deine Änderungen auf GitHub pushen. Mache dies spätestens am Ende dieses Praktikums nachdem du sichergestellt hast, dass deine R Markdown Datei zu einer HTML Datei rendert.
Hinweis: Der Begriff Rendern (Engl. to render; zu Deutsch: Bildsynthese) bezeichnet die Erstellung einer Grafik aus Rohdaten (wie z. B. Geoinformationen) oder einer Skizze. Quelle: Wikipedia